Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations3000
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory257.8 KiB
Average record size in memory88.0 B

Variable types

Numeric7
Categorical3

Alerts

carat is highly overall correlated with price and 3 other fieldsHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation

Reproduction

Analysis started2025-01-09 18:43:09.152421
Analysis finished2025-01-09 18:43:57.560699
Duration48.41 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

carat
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79416333
Minimum0.21
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:57.623295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.3
Q10.4
median0.7
Q31.05
95-th percentile1.71
Maximum3.5
Range3.29
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.4723126
Coefficient of variation (CV)0.5947298
Kurtosis1.0427297
Mean0.79416333
Median Absolute Deviation (MAD)0.32
Skewness1.1054686
Sum2382.49
Variance0.22307919
MonotonicityNot monotonic
2025-01-09T19:43:57.703306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 138
 
4.6%
0.31 122
 
4.1%
1.01 121
 
4.0%
0.7 100
 
3.3%
0.32 96
 
3.2%
0.9 84
 
2.8%
0.33 80
 
2.7%
0.41 79
 
2.6%
1 74
 
2.5%
0.71 74
 
2.5%
Other values (183) 2032
67.7%
ValueCountFrequency (%)
0.21 1
 
< 0.1%
0.23 23
 
0.8%
0.24 8
 
0.3%
0.25 13
 
0.4%
0.26 11
 
0.4%
0.27 9
 
0.3%
0.28 12
 
0.4%
0.29 7
 
0.2%
0.3 138
4.6%
0.31 122
4.1%
ValueCountFrequency (%)
3.5 1
< 0.1%
3 1
< 0.1%
2.77 1
< 0.1%
2.56 1
< 0.1%
2.54 1
< 0.1%
2.5 1
< 0.1%
2.46 1
< 0.1%
2.44 1
< 0.1%
2.42 1
< 0.1%
2.38 1
< 0.1%

cut
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
Ideal
1208 
Premium
765 
Very Good
674 
Good
268 
Fair
 
85

Length

Max length9
Median length7
Mean length6.291
Min length4

Characters and Unicode

Total characters18873
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Good
2nd rowPremium
3rd rowPremium
4th rowIdeal
5th rowGood

Common Values

ValueCountFrequency (%)
Ideal 1208
40.3%
Premium 765
25.5%
Very Good 674
22.5%
Good 268
 
8.9%
Fair 85
 
2.8%

Length

2025-01-09T19:43:57.776063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-09T19:43:57.858397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ideal 1208
32.9%
good 942
25.6%
premium 765
20.8%
very 674
18.3%
fair 85
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 2647
14.0%
d 2150
11.4%
o 1884
10.0%
m 1530
8.1%
r 1524
8.1%
a 1293
 
6.9%
I 1208
 
6.4%
l 1208
 
6.4%
G 942
 
5.0%
i 850
 
4.5%
Other values (6) 3637
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18873
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2647
14.0%
d 2150
11.4%
o 1884
10.0%
m 1530
8.1%
r 1524
8.1%
a 1293
 
6.9%
I 1208
 
6.4%
l 1208
 
6.4%
G 942
 
5.0%
i 850
 
4.5%
Other values (6) 3637
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18873
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2647
14.0%
d 2150
11.4%
o 1884
10.0%
m 1530
8.1%
r 1524
8.1%
a 1293
 
6.9%
I 1208
 
6.4%
l 1208
 
6.4%
G 942
 
5.0%
i 850
 
4.5%
Other values (6) 3637
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18873
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2647
14.0%
d 2150
11.4%
o 1884
10.0%
m 1530
8.1%
r 1524
8.1%
a 1293
 
6.9%
I 1208
 
6.4%
l 1208
 
6.4%
G 942
 
5.0%
i 850
 
4.5%
Other values (6) 3637
19.3%

color
Categorical

Distinct7
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size46.9 KiB
G
627 
E
571 
F
519 
H
486 
D
352 
Other values (2)
444 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2999
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowG
3rd rowF
4th rowF
5th rowH

Common Values

ValueCountFrequency (%)
G 627
20.9%
E 571
19.0%
F 519
17.3%
H 486
16.2%
D 352
11.7%
I 275
9.2%
J 169
 
5.6%
(Missing) 1
 
< 0.1%

Length

2025-01-09T19:43:57.928025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-09T19:43:57.987680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
g 627
20.9%
e 571
19.0%
f 519
17.3%
h 486
16.2%
d 352
11.7%
i 275
9.2%
j 169
 
5.6%

Most occurring characters

ValueCountFrequency (%)
G 627
20.9%
E 571
19.0%
F 519
17.3%
H 486
16.2%
D 352
11.7%
I 275
9.2%
J 169
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 627
20.9%
E 571
19.0%
F 519
17.3%
H 486
16.2%
D 352
11.7%
I 275
9.2%
J 169
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 627
20.9%
E 571
19.0%
F 519
17.3%
H 486
16.2%
D 352
11.7%
I 275
9.2%
J 169
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 627
20.9%
E 571
19.0%
F 519
17.3%
H 486
16.2%
D 352
11.7%
I 275
9.2%
J 169
 
5.6%

clarity
Categorical

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
SI1
759 
VS2
673 
SI2
477 
VS1
442 
VVS2
292 
Other values (3)
357 

Length

Max length4
Median length3
Mean length3.1203333
Min length2

Characters and Unicode

Total characters9361
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSI2
2nd rowVS1
3rd rowSI1
4th rowVS2
5th rowSI2

Common Values

ValueCountFrequency (%)
SI1 759
25.3%
VS2 673
22.4%
SI2 477
15.9%
VS1 442
14.7%
VVS2 292
 
9.7%
VVS1 213
 
7.1%
IF 104
 
3.5%
I1 40
 
1.3%

Length

2025-01-09T19:43:58.062103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-09T19:43:58.123088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
si1 759
25.3%
vs2 673
22.4%
si2 477
15.9%
vs1 442
14.7%
vvs2 292
 
9.7%
vvs1 213
 
7.1%
if 104
 
3.5%
i1 40
 
1.3%

Most occurring characters

ValueCountFrequency (%)
S 2856
30.5%
V 2125
22.7%
1 1454
15.5%
2 1442
15.4%
I 1380
14.7%
F 104
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2856
30.5%
V 2125
22.7%
1 1454
15.5%
2 1442
15.4%
I 1380
14.7%
F 104
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2856
30.5%
V 2125
22.7%
1 1454
15.5%
2 1442
15.4%
I 1380
14.7%
F 104
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2856
30.5%
V 2125
22.7%
1 1454
15.5%
2 1442
15.4%
I 1380
14.7%
F 104
 
1.1%

depth
Real number (ℝ)

Distinct110
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.738033
Minimum54.3
Maximum78.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:58.191623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum54.3
5-th percentile59.2
Q161.1
median61.9
Q362.5
95-th percentile63.7
Maximum78.2
Range23.9
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.4240316
Coefficient of variation (CV)0.023065711
Kurtosis8.0627226
Mean61.738033
Median Absolute Deviation (MAD)0.7
Skewness0.33429195
Sum185214.1
Variance2.0278661
MonotonicityNot monotonic
2025-01-09T19:43:58.259885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 136
 
4.5%
61.9 126
 
4.2%
62.1 121
 
4.0%
62.2 115
 
3.8%
62.4 113
 
3.8%
61.6 110
 
3.7%
61.5 102
 
3.4%
61.8 102
 
3.4%
62.3 99
 
3.3%
61.7 99
 
3.3%
Other values (100) 1877
62.6%
ValueCountFrequency (%)
54.3 1
 
< 0.1%
55.3 1
 
< 0.1%
56.2 1
 
< 0.1%
56.7 1
 
< 0.1%
56.8 2
 
0.1%
56.9 1
 
< 0.1%
57 3
0.1%
57.1 1
 
< 0.1%
57.2 5
0.2%
57.3 1
 
< 0.1%
ValueCountFrequency (%)
78.2 1
< 0.1%
69.7 1
< 0.1%
68.8 1
< 0.1%
68.3 1
< 0.1%
68.2 1
< 0.1%
67.9 1
< 0.1%
67.8 1
< 0.1%
67.7 1
< 0.1%
67.6 1
< 0.1%
66.8 1
< 0.1%

table
Real number (ℝ)

Distinct52
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.435433
Minimum52
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:58.324340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile54
Q156
median57
Q359
95-th percentile61
Maximum95
Range43
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.308361
Coefficient of variation (CV)0.040190539
Kurtosis23.532243
Mean57.435433
Median Absolute Deviation (MAD)1
Skewness2.0254125
Sum172306.3
Variance5.3285307
MonotonicityNot monotonic
2025-01-09T19:43:58.399825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 550
18.3%
56 544
18.1%
58 479
16.0%
55 359
12.0%
59 347
11.6%
60 239
8.0%
54 151
 
5.0%
61 110
 
3.7%
62 68
 
2.3%
63 29
 
1.0%
Other values (42) 124
 
4.1%
ValueCountFrequency (%)
52 4
 
0.1%
53 25
 
0.8%
53.5 2
 
0.1%
53.6 1
 
< 0.1%
53.7 2
 
0.1%
53.8 2
 
0.1%
53.9 2
 
0.1%
54 151
5.0%
54.1 4
 
0.1%
54.2 2
 
0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
66 9
 
0.3%
65 12
 
0.4%
64.3 1
 
< 0.1%
64 18
 
0.6%
63 29
 
1.0%
62 68
2.3%
61.2 1
 
< 0.1%
61 110
3.7%
60.7 1
 
< 0.1%

price
Real number (ℝ)

High correlation 

Distinct2191
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3887.0053
Minimum371
Maximum18731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:58.472157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum371
5-th percentile551.65
Q1955.5
median2369.5
Q35342.5
95-th percentile12757.8
Maximum18731
Range18360
Interquartile range (IQR)4387

Descriptive statistics

Standard deviation3944.0354
Coefficient of variation (CV)1.014672
Kurtosis2.3229782
Mean3887.0053
Median Absolute Deviation (MAD)1640.5
Skewness1.6430976
Sum11661016
Variance15555416
MonotonicityNot monotonic
2025-01-09T19:43:58.552971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605 11
 
0.4%
765 11
 
0.4%
687 9
 
0.3%
802 9
 
0.3%
789 8
 
0.3%
544 8
 
0.3%
625 8
 
0.3%
645 8
 
0.3%
828 7
 
0.2%
1013 7
 
0.2%
Other values (2181) 2914
97.1%
ValueCountFrequency (%)
371 1
 
< 0.1%
382 1
 
< 0.1%
386 1
 
< 0.1%
393 2
0.1%
394 4
0.1%
402 4
0.1%
404 1
 
< 0.1%
408 4
0.1%
412 1
 
< 0.1%
413 1
 
< 0.1%
ValueCountFrequency (%)
18731 1
< 0.1%
18717 1
< 0.1%
18604 1
< 0.1%
18525 1
< 0.1%
18522 1
< 0.1%
18515 1
< 0.1%
18493 1
< 0.1%
18475 1
< 0.1%
18430 1
< 0.1%
18374 1
< 0.1%

x
Real number (ℝ)

High correlation 

Distinct449
Distinct (%)15.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.7242948
Minimum3.89
Maximum9.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:58.625379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.89
5-th percentile4.29
Q14.715
median5.68
Q36.54
95-th percentile7.65
Maximum9.65
Range5.76
Interquartile range (IQR)1.825

Descriptive statistics

Standard deviation1.1187719
Coefficient of variation (CV)0.19544275
Kurtosis-0.68177371
Mean5.7242948
Median Absolute Deviation (MAD)0.92
Skewness0.42679852
Sum17167.16
Variance1.2516506
MonotonicityNot monotonic
2025-01-09T19:43:58.689260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.32 30
 
1.0%
4.44 25
 
0.8%
4.33 25
 
0.8%
4.28 24
 
0.8%
4.41 23
 
0.8%
5.7 23
 
0.8%
4.36 22
 
0.7%
4.37 21
 
0.7%
4.34 21
 
0.7%
4.35 21
 
0.7%
Other values (439) 2764
92.1%
ValueCountFrequency (%)
3.89 1
 
< 0.1%
3.9 3
0.1%
3.91 1
 
< 0.1%
3.92 3
0.1%
3.93 2
0.1%
3.94 4
0.1%
3.95 3
0.1%
3.96 4
0.1%
3.97 4
0.1%
3.98 1
 
< 0.1%
ValueCountFrequency (%)
9.65 1
< 0.1%
9.42 1
< 0.1%
8.93 1
< 0.1%
8.83 1
< 0.1%
8.82 1
< 0.1%
8.79 1
< 0.1%
8.76 1
< 0.1%
8.75 1
< 0.1%
8.62 1
< 0.1%
8.61 1
< 0.1%

y
Real number (ℝ)

High correlation 

Distinct452
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7271833
Minimum3.86
Maximum9.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:58.940829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.86
5-th percentile4.31
Q14.72
median5.68
Q36.53
95-th percentile7.6305
Maximum9.59
Range5.73
Interquartile range (IQR)1.81

Descriptive statistics

Standard deviation1.1104327
Coefficient of variation (CV)0.1938881
Kurtosis-0.6954507
Mean5.7271833
Median Absolute Deviation (MAD)0.91
Skewness0.42128093
Sum17181.55
Variance1.2330608
MonotonicityNot monotonic
2025-01-09T19:43:59.005109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.35 29
 
1.0%
4.38 26
 
0.9%
4.31 26
 
0.9%
4.33 26
 
0.9%
4.4 26
 
0.9%
4.32 24
 
0.8%
4.46 23
 
0.8%
4.41 23
 
0.8%
6.38 22
 
0.7%
4.39 21
 
0.7%
Other values (442) 2754
91.8%
ValueCountFrequency (%)
3.86 1
 
< 0.1%
3.9 1
 
< 0.1%
3.93 2
 
0.1%
3.94 1
 
< 0.1%
3.96 2
 
0.1%
3.97 2
 
0.1%
3.98 4
0.1%
3.99 2
 
0.1%
4 5
0.2%
4.01 5
0.2%
ValueCountFrequency (%)
9.59 1
< 0.1%
9.26 1
< 0.1%
8.83 2
0.1%
8.78 1
< 0.1%
8.76 1
< 0.1%
8.73 1
< 0.1%
8.69 1
< 0.1%
8.59 1
< 0.1%
8.58 1
< 0.1%
8.56 1
< 0.1%

z
Real number (ℝ)

High correlation 

Distinct295
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5332267
Minimum1.07
Maximum6.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2025-01-09T19:43:59.070591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.07
5-th percentile2.66
Q12.91
median3.51
Q34.04
95-th percentile4.73
Maximum6.03
Range4.96
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.6891764
Coefficient of variation (CV)0.19505581
Kurtosis-0.70181487
Mean3.5332267
Median Absolute Deviation (MAD)0.57
Skewness0.38923957
Sum10599.68
Variance0.47496411
MonotonicityNot monotonic
2025-01-09T19:43:59.135421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.69 43
 
1.4%
2.7 41
 
1.4%
2.68 41
 
1.4%
2.71 38
 
1.3%
2.74 37
 
1.2%
2.73 36
 
1.2%
4.05 35
 
1.2%
2.66 35
 
1.2%
2.72 34
 
1.1%
2.75 34
 
1.1%
Other values (285) 2626
87.5%
ValueCountFrequency (%)
1.07 1
 
< 0.1%
2.29 1
 
< 0.1%
2.36 1
 
< 0.1%
2.37 1
 
< 0.1%
2.39 1
 
< 0.1%
2.4 2
 
0.1%
2.42 4
0.1%
2.43 7
0.2%
2.44 5
0.2%
2.45 3
0.1%
ValueCountFrequency (%)
6.03 1
< 0.1%
5.58 1
< 0.1%
5.56 1
< 0.1%
5.48 1
< 0.1%
5.41 1
< 0.1%
5.37 2
0.1%
5.3 1
< 0.1%
5.29 1
< 0.1%
5.28 1
< 0.1%
5.27 1
< 0.1%

Interactions

2025-01-09T19:43:52.272703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:09.492211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:14.011348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:18.417361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:23.168315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:41.865368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:47.543986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:52.333783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:09.657295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:14.059082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:18.462629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:25.414696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:42.219396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:47.592839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:52.388341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:09.746626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:14.106083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:18.505305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:27.552804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:42.423030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:47.638943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:52.500742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:09.802664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:14.151271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:18.553297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:29.694259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:42.730347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:47.687746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:56.966524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:13.679865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:17.938922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:22.767934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:35.116502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:46.534630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:51.784189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:57.219379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:13.914079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:18.322393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:23.059540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:37.428591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:47.027370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:52.170738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:57.269097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:13.961126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:18.368508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:23.116029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:39.679780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:47.336930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-09T19:43:52.218804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-09T19:43:59.233409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
caratclaritycolorcutdepthpricetablexyz
carat1.0000.1710.1320.1170.0200.9650.1950.9970.9960.995
clarity0.1711.0000.0730.1140.1080.2230.0740.1830.1830.192
color0.1320.0731.0000.0360.0000.2370.0310.1430.1350.140
cut0.1170.1140.0361.0000.3860.2240.3230.2670.1130.132
depth0.0200.1080.0000.3861.0000.004-0.231-0.032-0.0350.089
price0.9650.2230.2370.2240.0041.0000.1690.9650.9650.960
table0.1950.0740.0310.323-0.2310.1691.0000.2000.1950.163
x0.9970.1830.1430.267-0.0320.9650.2001.0000.9980.989
y0.9960.1830.1350.113-0.0350.9650.1950.9981.0000.988
z0.9950.1920.1400.1320.0890.9600.1630.9890.9881.000

Missing values

2025-01-09T19:43:57.349322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-09T19:43:57.442162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-09T19:43:57.525534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

caratcutcolorclaritydepthtablepricexyz
151101.25Very GoodESI260.855.060736.947.004.24
3110.74PremiumGVS162.960.028005.745.683.59
532610.74PremiumFSI161.459.026485.815.823.57
341960.33IdealFVS261.955.08544.464.432.75
20940.90GoodHSI260.461.031146.146.223.73
233001.52IdealHVS261.855.1113337.387.424.58
106321.01Very GoodESI163.259.048306.286.253.96
437540.61PremiumIVS261.856.014385.445.413.35
255622.14GoodHSI157.560.0143958.578.484.90
51200.91Very GoodHSI162.756.037626.146.183.86
caratcutcolorclaritydepthtablepricexyz
173631.20IdealHSI161.156.069686.926.874.21
509220.70IdealFSI161.656.023195.735.673.51
341340.30IdealFVVS162.054.28544.314.332.68
223992.77PremiumHI162.662.0104248.938.835.56
242391.90IdealHVS261.755.0124437.97.814.85
55410.71IdealDVVS261.357.038565.75.813.53
130011.22PremiumIVS260.158.054056.927.004.18
19920.91PremiumFSI262.156.030966.266.213.87
522310.72PremiumHVS261.457.024845.795.753.54
339920.42IdealGVS262.454.08474.784.833.00